Though dialectal language is increasingly abundant on social media, few
resources exist for developing NLP tools to handle such language. We conduct a
case study of dialectal language in online conversational text by investigating
African-American English (AAE) on Twitter. We propose a distantly supervised
model to identify AAE-like language from demographics associated with
geo-located messages, and we verify that this language follows well-known AAE
linguistic phenomena. In addition, we analyze the quality of existing language
identification and dependency parsing tools on AAE-like text, demonstrating
that they perform poorly on such text compared to text associated with white
speakers. We also provide an ensemble classifier for language identification
which eliminates this disparity and release a new corpus of tweets containing
AAE-like language.

本文研究表明，虽然方言语言在社交媒体上越来越普遍，但是开发自然语言处理工具的资源很少。本文以推特上的非裔美国英语为案例研究方言语言的实用性。文章提出了一种基于地理位置信息的远距离语言学习模型来识别 AAEL（African-American English like language），并验证了这种语言遵循已知的非裔美国英语语言现象。此外，文章对现有的语言识别和依赖解析工具在 AAEL 文本上的质量进行了分析，证明它们在处理这样的文本时比白人说话者的文本表现差。作者还提供了一种语言识别的整合分类器，消除了这种差异，并发布了一个包含 AAEL 类似语言的新推文语料库。